Control Networks

The research focuses on the analysis of the main theoretical properties of this class of systems, with possible applications in the context of genetic regulation networks. Ongoing projects are the following:

Dense camera networks: 3D reconstruction in motion capture systems shows critical issues when scaling with the number of cameras or the complexity of the scene. In this context a distributed approach is proposed to solve the multicamera reconstruction problem in large scale motion capture systems.

The research focuses on networked control systems which are systems composed of physically distributed smart agents that can sense the environment, act on it, and communicate with one other through a communication network to achieve a common goal. The challenges reside in the design of control systems that are robust to communication constraints like bandwidth, random delay and packet loss, to computational constraints due to the large amount of data to be processed or to the distributed nature of the sensing and control, to real-time implementation on limited resources devices, an to complexity to the large number of possibly unreliable agents involved. Ongoing specific reseach topics are:

Asynchronous consensus algorithms

Distributed convex optimization

Area-based asynchronous estimation

Clock synchronization in wireless sensor networks

Distributed algorithms robust to unreliable communication

Distributed blind sensor calibration

Control and estimation subject to random delay, packet loss and quantization